1. Field of the Invention
The invention relates to characterization of features in images of biological tissue, particularly to detection of pulmonary nodules in chest radiographs, and relates to CAD techniques for automated detection of abnormalities in chest images.
The present invention also generally relates to CAD techniques for automated detection of abnormalities in digital images, for example as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,668,888; 5,673,332; 5,740,268; 5,790,690; 5,832,103; 5,873,824; and 5,881,124; as well as U.S. patent application Ser. Nos. 08/158,388; 08/173,935; 08/523,210; 08/562,087; 08/757,611; 08/900,191; 08/900,188; 08/900,192; 08/900,189 and 08/398,307 which has been abandoned in favor of U.S. continuation patent application Ser. No. 08/982,282. The present invention includes use of various technologies referenced and described therein, as well as described in the references identified in the appended APPENDIX and cross-referenced throughout the specification be reference to the number, in brackets, of the respective reference listed in the APPENDIX, the entire contents of which, including the related patents and applications listed above and references listed in the APPENDIX, are incorporated herein by reference.
2. Discussion of the Related Art
Certain diseases, e.g., cancer, can form nodules (i.e., abnormal, often rounded growths) in body tissues. Detection of nodules (which can be, e.g., malignant or benign tumors) can be of great importance for diagnosis of disease, particularly lung cancer. Although X-radiographs (i.e., x-ray images) have, in some cases, proven successful in detecting the nodules, studies have shown that radiologists attempting to diagnose lung disease by visual examination of chest radiographs can fail to detect pulmonary (i.e., lung) nodules in up to 30% of actually abnormal cases (i.e., cases in which nodules are actually present). [1] [2] To improve the accuracy of diagnoses, computer-aided diagnosis (CAD) of X-radiographs has been developed by the inventors and others at the Department of Radiology at the University of Chicago, and utilized in conjunction with visual examination of the X-ray images. [3] [4] [5] The output of the computer alerts the radiologist to potential nodule locations, and the final diagnostic decision is then made by the radiologist. The feasibility of CAD to improve the performance of radiologists has been demonstrated in the detection of pulmonary nodules. [6]
However, conventional techniques for computerized detection of pulmonary nodules suffer from detection of "false positives" (i.e., spurious detection of nodules that do not actually exist). In conventional systems, reduced rates of false positive detection cannot typically be achieved without reducing the sensitivity of detection of actual nodules. Consequently, operating a conventional system at a sensitivity sufficiently high for clinical use (i.e., practical medical use) has the drawback that the number of false positives can be undesirably high. In fact, some conventional systems, if operated at acceptably high sensitivity, can produce from 5 to 10 false positives per image.
Therefore, there is a need for an apparatus and method which can maintain a high sensitivity of detection of actual nodules in biological tissue, while reducing the rate of spurious detection. In particular, increased accuracy of pulmonary nodule detection is important for correct diagnosis of lung disease.